Delete loading script

code_x_glue_cc_clone_detection_big_clone_bench.py

@@ -1,95 +0,0 @@
-from typing import List
-
-import datasets
-
-from .common import TrainValidTestChild
-from .generated_definitions import DEFINITIONS
-
-
-_DESCRIPTION = """Given two codes as the input, the task is to do binary classification (0/1), where 1 stands for semantic equivalence and 0 for others. Models are evaluated by F1 score.
-The dataset we use is BigCloneBench and filtered following the paper Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree."""
-
-_CITATION = """@inproceedings{svajlenko2014towards,
-title={Towards a big data curated benchmark of inter-project code clones},
-author={Svajlenko, Jeffrey and Islam, Judith F and Keivanloo, Iman and Roy, Chanchal K and Mia, Mohammad Mamun},
-booktitle={2014 IEEE International Conference on Software Maintenance and Evolution},
-pages={476--480},
-year={2014},
-organization={IEEE}
-}
-
-@inproceedings{wang2020detecting,
-title={Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree},
-author={Wang, Wenhan and Li, Ge and Ma, Bo and Xia, Xin and Jin, Zhi},
-booktitle={2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)},
-pages={261--271},
-year={2020},
-organization={IEEE}
-}"""
-
-
-class CodeXGlueCcCloneDetectionBigCloneBenchImpl(TrainValidTestChild):
-    _DESCRIPTION = _DESCRIPTION
-    _CITATION = _CITATION
-
-    _FEATURES = {
-        "id": datasets.Value("int32"),  # Index of the sample
-        "id1": datasets.Value("int32"),  # The first function id
-        "id2": datasets.Value("int32"),  # The second function id
-        "func1": datasets.Value("string"),  # The full text of the first function
-        "func2": datasets.Value("string"),  # The full text of the second function
-        "label": datasets.Value("bool"),  # 1 is the functions are not equivalent, 0 otherwise
-    }
-
-    _SUPERVISED_KEYS = ["label"]
-
-    def generate_urls(self, split_name):
-        yield "index", f"{split_name}.txt"
-        yield "data", "data.jsonl"
-
-    def _generate_examples(self, split_name, file_paths):
-        import json
-
-        js_all = {}
-
-        with open(file_paths["data"], encoding="utf-8") as f:
-            for idx, line in enumerate(f):
-                entry = json.loads(line)
-                js_all[int(entry["idx"])] = entry["func"]
-
-        with open(file_paths["index"], encoding="utf-8") as f:
-            for idx, line in enumerate(f):
-                line = line.strip()
-                idx1, idx2, label = [int(i) for i in line.split("\t")]
-                func1 = js_all[idx1]
-                func2 = js_all[idx2]
-
-                yield idx, dict(id=idx, id1=idx1, id2=idx2, func1=func1, func2=func2, label=(label == 1))
-
-
-CLASS_MAPPING = {
-    "CodeXGlueCcCloneDetectionBigCloneBench": CodeXGlueCcCloneDetectionBigCloneBenchImpl,
-}
-
-
-class CodeXGlueCcCloneDetectionBigCloneBench(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIG_CLASS = datasets.BuilderConfig
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name=name, description=info["description"]) for name, info in DEFINITIONS.items()
-    ]
-
-    def _info(self):
-        name = self.config.name
-        info = DEFINITIONS[name]
-        if info["class_name"] in CLASS_MAPPING:
-            self.child = CLASS_MAPPING[info["class_name"]](info)
-        else:
-            raise RuntimeError(f"Unknown python class for dataset configuration {name}")
-        ret = self.child._info()
-        return ret
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        return self.child._split_generators(dl_manager=dl_manager)
-
-    def _generate_examples(self, split_name, file_paths):
-        return self.child._generate_examples(split_name, file_paths)